3 citations · 6 across the 6 of their papers we have counts for
6 papers
A Review of Adversarial Attacks in Computer Vision
Yutong Zhang, Yao Li, Yin Li +1
Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened…
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Jie Ning, Jiebao Sun, Yao Li +2
Deep neural networks (DNNs) have shown superior performance comparing to traditional image denoising algorithms. However, DNNs are inevitably vulnerable while facing adversarial at…
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer
Enzhe Zhao, Zhichang Guo, Shengzhu Shi +3
In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling…
Stationary Point Losses for Robust Model
Weiwei Gao, Dazhi Zhang, Yao Li +2
The inability to guarantee robustness is one of the major obstacles to the application of deep learning models in security-demanding domains. We identify that the most commonly use…
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
Xiang Wang, Yimin Yang, Zhichang Guo +4
Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perfor…
A new collision avoidance model with random batch resolution strategy
Tianlu Chen, Chang Yang, Léon Matar Tine +1
Research on crowd simulation has important and wide range of applications. The main difficulty is how to lead all particles with a same and simple rule, especially when particles a…